Abstract
AIM: The aim of the present study was to build a clinical decision support system (CDSS) that can predict the presence of diabetic retinopathy (DR) in type 1 diabetes (T1DM) patients. MATERIAL AND METHOD: We built two versions of our CDSS to predict the presence of any-type DR and sight-threatening DR (STDR) in T1DM patients. The first version was trained using 324 T1DM and 826 T2DM patients. The second was trained with only the 324 T1DM patients. RESULTS: The first version achieved an accuracy (ACC) = 0.795, specificity (SP) = 83%, and sensitivity (S) = 65.7% to predict the presence of any-DR, and an ACC = 0.918, SP = 87.1% and S = 87.8% for STDR. The second model achieved ACC = 0.799, SP = 87.5% and S = 86.3% when predicting any-DR and ACC = 0.937, SP = 90.9% and S = 83.0% for STDR. CONCLUSION: The two models better predict STDR than any-DR in T1DM patients. We will need a larger sample to strengthen our results.